Hydrologically enhanced machine learning framework for urban flood inundation mapping using multi-sensor remote sensing data: a case study of Mumbai, India
- 1Sandip Institute of Technology & Research Centre (Nashik, India)
Abstract
Mapping urban flooding in rapidly urbanizing coastal megacities remains challenging due to complex terrain conditions, high population density, and the limited availability of reliable ground observations. This study presents a hydrologically enhanced machine-learning framework for automated urban flood inundation mapping by integrating multi-sensor satellite observations within a scalable decision-support system. A comprehensive predictor dataset was generated using Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 optical imagery, Shuttle Radar Topography Mission (SRTM) digital elevation data, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall products through Google Earth Engine. To explicitly represent hydrologic controls on flood propagation that are often overlooked in purely data-driven approaches, two hydrologically informed predictors were incorporated: the Relative Elevation Model (REM), which characterizes terrain-controlled flood accumulation, and the Rainfall-Normalized Index (RNI), which represents rainfall-conditioned surface wetness. A flood inventory was generated by integrating SAR backscatter change detection, optical water indices, and topographic constraints, resulting in approximately 2.6 × 10 ⁵ flooded pixels across the Mumbai Metropolitan Region during the 2019 monsoon season. A balanced stratified sampling strategy was employed to create representative training and testing datasets for supervised classification. Random Forest, optimized Extreme Gradient Boosting (XGBoost), and ensemble models were developed and evaluated using standard classification metrics. The optimized XGBoost model achieved the highest predictive performance, with an overall accuracy of 71.7 % and an area under the receiver operating characteristic curve (AUC) of 0.803, outperforming both the Random Forest and ensemble models. The DeLong test indicated statistically significant differences in AUC for the evaluated hydrologic-predictor ablation comparisons at the 95 % confidence level. Feature ablation analysis further demonstrated the importance of hydrologic-topographic predictors, with REM and RNI contributing an increase of approximately 5 %–6 % in AUC. The inclusion of hydrologic predictors improved model discrimination from approximately 0.66 using SAR-only predictors to approximately 0.80 using the complete predictor framework. The resulting flood maps exhibited spatial patterns that were hydrologically consistent with major drainage networks and low-lying areas across the study region. The proposed framework provides a reproducible, scalable, and hydrologically informed and physically interpretable predictors approach for urban flood inundation mapping and demonstrates considerable potential for operational flood monitoring, hazard assessment, and decision-support applications in data-limited tropical urban environments.
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BibTeX
@article{pawar2026hydrologically,
title = {{Hydrologically enhanced machine learning framework for urban flood inundation mapping using multi-sensor remote sensing data: a case study of Mumbai, India}},
author = {Ankush S. Pawar and Gayatri M. Phade},
journal = {Natural Hazards and Earth System Sciences},
year = {2026},
month = oct,
doi = {10.5194/nhess-26-5119-2026},
url = {https://nhess.copernicus.org/articles/26/5119/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Hydrologically enhanced machine learning framework for urban flood inundation mapping using multi-sensor remote sensing data: a case study of Mumbai, India
AU - Ankush S. Pawar
AU - Gayatri M. Phade
PY - 2026
DA - 2026/10/20
JO - Natural Hazards and Earth System Sciences
DO - 10.5194/nhess-26-5119-2026
UR - https://nhess.copernicus.org/articles/26/5119/2026/
AB - Mapping urban flooding in rapidly urbanizing coastal megacities remains challenging due to complex terrain conditions, high population density, and the limited availability of reliable ground observations. This study presents a hydrologically enhanced machine-learning framework for automated urban flood inundation mapping by integrating multi-sensor satellite observations within a scalable decision-support system. A comprehensive predictor dataset was generated using Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 optical imagery, Shuttle Radar Topography Mission (SRTM) digital elevation data, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall products through Google Earth Engine. To explicitly represent hydrologic controls on flood propagation that are often overlooked in purely data-driven approaches, two hydrologically informed predictors were incorporated: the Relative Elevation Model (REM), which characterizes terrain-controlled flood accumulation, and the Rainfall-Normalized Index (RNI), which represents rainfall-conditioned surface wetness. A flood inventory was generated by integrating SAR backscatter change detection, optical water indices, and topographic constraints, resulting in approximately 2.6 × 10 ⁵ flooded pixels across the Mumbai Metropolitan Region during the 2019 monsoon season. A balanced stratified sampling strategy was employed to create representative training and testing datasets for supervised classification. Random Forest, optimized Extreme Gradient Boosting (XGBoost), and ensemble models were developed and evaluated using standard classification metrics. The optimized XGBoost model achieved the highest predictive performance, with an overall accuracy of 71.7 % and an area under the receiver operating characteristic curve (AUC) of 0.803, outperforming both the Random Forest and ensemble models. The DeLong test indicated statistically significant differences in AUC for the evaluated hydrologic-predictor ablation comparisons at the 95 % confidence level. Feature ablation analysis further demonstrated the importance of hydrologic-topographic predictors, with REM and RNI contributing an increase of approximately 5 %–6 % in AUC. The inclusion of hydrologic predictors improved model discrimination from approximately 0.66 using SAR-only predictors to approximately 0.80 using the complete predictor framework. The resulting flood maps exhibited spatial patterns that were hydrologically consistent with major drainage networks and low-lying areas across the study region. The proposed framework provides a reproducible, scalable, and hydrologically informed and physically interpretable predictors approach for urban flood inundation mapping and demonstrates considerable potential for operational flood monitoring, hazard assessment, and decision-support applications in data-limited tropical urban environments.
SN - 1684-9981
ER -